Skip to main content

MLWC MOOC 3: Applications of Machine Learning in Weather and Climate

Six modules giving ML applications in observations, forecasting, data assimilation, post-processing, ocean and more.


Last updated: 16 December 2025
Enrol now! Enrolled students: 1K+

25 hours

Multi-lesson course

What you'll learn

  • The application of ML methods to a range of problems in weather and climate
  • The details of practical code examples which can be used to apply to your own problems

Prerequisites

Please complete MOOC MLWC - 2. Concepts of Machine Learning first, or ensure you are familiar with the topics covered there. Intermediate proficiency with Python, knowledge of statistics and experience in weather/climate is assumed.

Topics

  • Climate
  • Machine learning

Description

In this third tier of our MOOC on Machine Learning (ML) in Weather and Climate, we focus on the implementation of ML in weather and climate problems.

This six-module course gives code examples and explainers in topics including:

  • Satellite precipitation removal
  • Environmental modelling
  • Nowcasting
  • Data assimilation
  • Downscaling
  • Ocean modelling
  • Operational meteorology

This course is aimed at those with a technical/weather background and experience in Python. Ideally you will have already taken Tiers 1 and 2 of the MOOC on ML in Weather and Climate, or already be familiar with the concepts there. 

Please note that this course ran live in early 2023 and reflected the state of the art at that point in time.

Course Content

  • How to build a simple ANN model for satellite precipitation retrieval? 🕓30 min
  • Quiz
  • Machine learning for environmental modelling 🕓30 min
  • Quiz
  • E-learning 🕓30 min
  • Quiz
  • E-learning 🕓1 hour
  • E-learning 🕓45 min
  • E-learning 🕓 30 min
  • Quiz
  • Operational Meteorology 🕓30 min

Tags

  • data assimilation
  • downscaling
  • nowcasting
  • observations
  • ocean

Start this course

Self enrolment (Student)
Self enrolment (Student)
Back